Mental Workload as a Predictor of ATCO’s Performance: Lessons Learnt from ATM Task-Related Experiments

Muñoz-de-Escalona, Enrique; Leva, Maria Chiara; Cañas, José Juan · 2024 · Crossref

DOI: 10.3390/aerospace11080691

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Summary

This study investigates whether mental workload measurements can serve as more effective predictors of Air Traffic Controller (ATCO) performance and task complexity than traditional environmental parameters, such as air-traffic density. The research is motivated by the recognition that ATCO mental workload is the primary functional limitation on Air Traffic Management (ATM) system capacity, yet current methodologies often rely on simplistic metrics that fail to capture the multifactorial nature of task complexity. The authors aim to determine if subjective and psychophysiological workload indicators can predict performance degradation and complexity peaks, while also examining latency differences between various measurement methods. The experimental design involved participants performing a 120-minute ATM simulation using the ATC Lab-Advanced software. The scenario was programmed to vary task demands over time, requiring participants to manage 70 aircraft (50 inbound, 20 outbound) to avoid conflicts. Three primary mental workload measures were recorded: conflict rate (performance), pupil size (psychophysiological, via Tobii T120 eye-tracker), and the Instantaneous Self-Assessment (ISA) scale (subjective). The study utilized two experimental conditions to test data granularity: 5-minute intervals and 2-minute intervals for averaging data. The researchers tested three hypotheses: that the ISA scale and pupillometry could predict performance and task complexity, and that latency differences exist among these measures. The findings indicate that mental workload measurements are superior predictors of poor performance and high task complexity peaks compared to established environmental factors. Specifically, the study highlights that while traditional models rely on static parameters like traffic density, dynamic workload metrics better reflect the cognitive complexity of the scenario. The results also address the issue of "dissociation" between workload measures, suggesting that temporal lags (latency) between subjective, physiological, and performance indicators contribute to inconsistencies in workload assessment. By identifying these latency differences, the study provides a framework for understanding how different ATM factors influence overall task complexity. The significance of this work lies in its potential to advance the development of computational models for ATM, such as COMETA, which aim to monitor and predict ATCO workload in real-time. By demonstrating that workload metrics outperform basic density-based formulas, the research supports the integration of multi-modal workload monitoring into ATM systems. This approach could enhance aviation safety by enabling adaptive airspace restructuring and targeted training, allowing ATCOs and systems to better anticipate and manage varying task demands. The study underscores the need for robust, convergent methodologies in workload assessment to address the complex, dynamic, and uncertain nature of air traffic control tasks.

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Topics

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• workload measurement
• mental demand

Information type

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• Empirical Findings: self report data, physiological data
• Theoretical Contribution: theory or model